Emergence@ASU graduate students Harrison Smith and Tessa Fisher are attending a NASA-sponsored "workshop without walls" as part of the NASA Nexus for Exoplanet System Science (NExSS). The workshop isaimed at setting future directions in exoplanet biosignatures and has been profiled in Nature, and Scientific American. Emergence@ASU PI Sara Walker will deliver a plenary talk on "Statistical Signatures of Life" as part of the workshop. The full workshop agenda is available here and is open for remote participation!

One of the most remarkable features of the > 3.5 billion year history of life on Earth is the apparent trend of innovation and open-ended growth of complexity. Similar trends are apparent in artificial and technological systems. However, a general framework for understanding open-ended evolution as it might occur in biological or technological systems has not yet been achieved. Here, we cast the problem within the broader context of dynamical systems theory to uncover and characterize mechanisms for producing open-ended evolution (OEE). We present formal definitions of two hallmark features of OEE: unbounded evolution and innovation. We define unbounded evolution as patterns that are non-repeating within the expected Poincare\'e recurrence time of an equivalent isolated system, and innovation as trajectories not observed in isolated systems. As a case study, we test three new variants of cellular automata (CA) that implement time-dependent update rules against these two definitions. We find that each is capable of generating conditions for OEE, but vary in their ability to do so. Our results demonstrate that state-dependent dynamics, widely regarded as a hallmark feature of life, statistically out-perform other candidate mechanisms. It is also the only mechanism to produce OEE in a scalable manner, consistent with notions of OEE as ongoing production of complexity. Our results thereby suggest a new framework for unifying the mechanisms for generating OEE with features distinctive to life and its artifacts, with wide applicability to both biological and artificial systems.